A large number of algorithms designed to find military targets have been published in the literature, using a wide variety of techniques. Representative examples of these algorithms are discussed in the following three ATR survey papers that have been published in the literature, all of which are incorporated herein by reference:    1. J. Ratches et al, “Aided and Automatic Target Recognition Based Upon Sensory Inputs from Image Forming Systems”, IEEE Transactions on Pattern Analysis and Machine Intelligence, September 1997.    2. M. Roth, “Survey of Neural Network Technology for Automatic Target Recognition”, IEEE Transactions on Neural Networks, March 1990.    3. B. Bhanu, “Automatic Target Recognition: State of the Art Survey”, IEEE Transactions on Aerospace and Electronic Systems, July 1986.
While most automatic target detection/recognition (ATD/R) algorithms use much problem specific knowledge to improve performance, the result is an algorithm that is tailored to specific target types, and poses. The approximate range to target is often required, with varying amounts of tolerance.
For example, in some scenarios, it is assumed that the range is known to within a meter from a laser range finder or a digital map. In other scenarios, only the range to the center of field of view and the depression angle is known, so that a flat earth approximation provides a rough estimate. Many algorithms, both model based and learning based, either require accurate range information, or compensate for inaccurate information by attempting to detect targets at a number of different ranges within the tolerance of the range.
Because many such algorithms are quite sensitive to scale, even a modest range tolerance requires that the algorithm attempt to match at a large number of closely spaced scales, driving up both the computational complexity and the false alarm rate. Algorithms have often used view-based neural networks or statistical methods.